library(ggplot2)
library(stringi)
library(gridExtra)
library(kableExtra)
library(psych)
library(limma)
library(tidyverse)
library(CONSTANd) # install from source: https://github.com/PDiracDelta/CONSTANd/
library(NOMAD) # devtools::install_github("carlmurie/NOMAD")
This notebook presents isobaric labeling data analysis strategy that includes data-driven normalization.
We will check how varying analysis components [summarization/normalization/differential abundance testing methods] changes end results of a quantitative proteomic study.
source('./other_functions.R')
source('./plotting_functions.R')
# you should either make a symbolic link in this directory
study.design=read.delim('msstatstmt_studydesign.csv')
data.list <- readRDS('input_data.rds')
dat.l <- data.list$dat.l # data in long format
# dat.w <- data.list$dat.w # data in wide format
if ('X' %in% colnames(dat.l)) { dat.l$X <- NULL }
# remove shared peptides
shared.peptides <- dat.l %>% filter(!shared.peptide)
# keep spectra with isolation interference <30 and no missing quantification channels
dat.l <- dat.l %>% filter(isoInterOk & noNAs)
# which proteins were spiked in?
spiked.proteins <- dat.l %>% distinct(Protein) %>% filter(stri_detect(Protein, fixed='ups')) %>% pull %>% as.character
# which peptides were identified in each MS run?
unique.pep=dat.l %>%
group_by(Run) %>%
distinct(Peptide) %>%
mutate(val=1)
unique.pep <- xtabs(val~Peptide+Run, data=unique.pep)
tmp <- apply(unique.pep, 1, function(x) all(x==1))
inner.peptides <- rownames(unique.pep)[tmp]
# specify # of varying component variants and their names
n.comp.variants <- 3
variant.names <- c('CONSTANd', 'NOMAD', 'medianSweeping')
scale.vec <- c('log', 'log', 'log')
# pick reference channel and condition for making plots / doing DEA
quanCols <- unique(dat.l$Channel)
referenceChannel <- '127C'
referenceCondition <- '0.5'
dat.unit.l <- dat.l %>% mutate(response=Intensity) %>% select(-Intensity)
# dat.unit.l <- dat.l %>% mutate(response=log2(Intensity)) %>% select(-Intensity)
# switch to wide format
dat.unit.w <- pivot_wider(data = dat.unit.l, id_cols=-one_of(c('Condition', 'BioReplicate')), names_from=Channel, values_from=response)
# dat.unit.w2 <- lapply(dat.unit.l, function(x) {
# pivot_wider(data = x, id_cols=-one_of(c('Condition', 'BioReplicate')), names_from=c('Run','Channel'), values_from=response)
# })
dat.norm.w <- emptyList(variant.names)
# dat.unit.l entries are in long format so all have same colnames and no quanCols
x.split <- split(dat.unit.w, dat.unit.w$Run) # apply CONSTANd to each Run separately
x.split.norm <- lapply(x.split, function(y) {
y[,quanCols] <- CONSTANd(y[,quanCols])$normalized_data
return(y)
})
dat.norm.w$CONSTANd <- bind_rows(x.split.norm)
We apply NOMAD on the PSM level instead of the peptide level.
# doRobust=F: use means, like CONSTANd; doLog=F: values are already transformed.
dat.nomadnorm <- nomadNormalization(dat.unit.l$response, dat.unit.l %>% rename(iTRAQ=Channel), doRobust = FALSE, doiTRAQCorrection = FALSE, doLog = TRUE)
## Running normalization with 464224 number of data points
## Normalizing for factor: Peptide
## Normalizing for factor: Run
## Normalizing for factor: iTRAQ
## Normalizing for factor: Run Peptide
## Normalizing for factor: Run iTRAQ
dat.nomadnorm$x$response <- dat.nomadnorm$y
dat.norm.w$NOMAD <- pivot_wider(data = dat.nomadnorm$x, id_cols=-one_of(c('Condition', 'BioReplicate')), names_from=iTRAQ, values_from=response)
# get rid of factors
#factornames <- names(dat.norm.w$NOMAD)[sapply(dat.norm.w$NOMAD, is.factor)]
#dat.norm.w$NOMAD <- dat.norm.w$NOMAD %>% mutate(across(factornames, remove_factors))
# subtract the spectrum median log2intensity from the observed log2intensities
dat.norm.w$medianSweeping <- dat.unit.w
dat.norm.w$medianSweeping[,quanCols] <- sweep(dat.norm.w$medianSweeping[,quanCols], 1, apply(dat.norm.w$medianSweeping[,quanCols], 1, median) )
Summarize quantification values from PSM to peptide (first step) to protein (second step).
# normalized data
dat.norm.summ.w <- lapply(dat.norm.w, function(x) {
# group by (run,)protein,peptide then summarize twice (once on each level)
# add select() statement because summarise_at is going bananas over character columns
y <- x %>% group_by(Run, Protein, Peptide) %>% select(Run, Protein, Peptide, quanCols) %>% summarise_at(.vars = quanCols, .funs = median) %>% select(Run, Protein, quanCols) %>% summarise_at(.vars = quanCols, .funs = median) %>% ungroup()
return(y)
})
Notice that the row sums are not equal to Ncols anymore, because the median summarization does not preserve them (but mean summarization does).
Let’s also summarize the non-normalized data for comparison in the next section.
# non-normalized data
# add select() statement because summarise_at is going bananas over character columns
dat.nonnorm.summ.w <- dat.unit.w %>% group_by(Run, Protein, Peptide) %>% select(Run, Protein, Peptide, quanCols) %>% summarise_at(.vars = quanCols, .funs = median) %>% select(Run, Protein, quanCols) %>% summarise_at(.vars = quanCols, .funs = median) %>% ungroup()
# medianSweeping: in each channel, subtract median computed across all proteins within a channel
# do the above separately for each MS run
x.split <- split(dat.norm.summ.w$medianSweeping, dat.norm.summ.w$medianSweeping$Run)
x.split.norm <- lapply(x.split, function(y) {
y[,quanCols] <- sweep(y[,quanCols], 2, apply(y[,quanCols], 2, median) )
return(y)
})
dat.norm.summ.w$medianSweeping <- bind_rows(x.split.norm)
# make data completely wide (also across runs)
## non-normalized data
dat.nonnorm.summ.w2 <- dat.nonnorm.summ.w %>% pivot_wider(names_from = Run, values_from = all_of(quanCols))
## normalized data
dat.norm.summ.w2 <- lapply(dat.norm.summ.w, function(x) {
dat.tmp <- x %>% pivot_wider(names_from = Run, values_from = all_of(quanCols))
return(dat.tmp)
})
# make vectors with condition labels and color coding corresponding to samples in wide format data
colors.condition <- tribble(
~Condition, ~Col,
"0.125", 'black',
"0.5", 'blue',
"0.667", 'green',
"1", 'red'
)
run_channel_condition <- expand_grid(Channel=quanCols, Run=unique(study.design$Run)) %>% left_join(study.design, by=c('Channel', 'Run')) %>% select(Run, Channel, Condition)
colors.condition.map <- run_channel_condition %>% unite(Channel, Channel:Run) %>% left_join(colors.condition, by='Condition')
ord <- match(colnames(dat.norm.summ.w2[[1]]), colors.condition.map$Channel)
ord <- ord[!is.na(ord)] # drop first entry which is NA
# important: these two vectors contain colors and condition labels corresponding to data in wide2 format
cols.vec <- colors.condition.map[ord, 'Col'] %>% pull
conditions.vec <- colors.condition.map[ord, 'Condition'] %>% pull
# use (half-)wide format
par(mfrow=c(2,2))
boxplot.w(dat.nonnorm.summ.w,study.design, 'Raw')
for (i in 1: n.comp.variants){
boxplot.w(dat.norm.summ.w[[i]], study.design, paste('Normalized', variant.names[i], sep='_'))
}
par(mfrow=c(1,1))
MA plots of two single samples taken from condition 1 and condition 0.125, measured in different MS runs (samples Mixture2_1:127C and Mixture1_2:129N, respectively).
# different unit variants require different computation of fold changes and average abuandance: additive or multiplicative scale; see maplot.ils function
# use wide2 format
p <- vector('list', n.comp.variants+1)
p[[1]] <- maplot.ils(dat.nonnorm.summ.w2, '127C_Mixture2_1', '129N_Mixture1_2', scale.vec[i], paste('Raw', variant.names[i], sep='_'))
for (i in 1: n.comp.variants){
p[[i+1]]<- maplot.ils(dat.norm.summ.w2[[i]], '127C_Mixture2_1', '129N_Mixture1_2', scale.vec[i], paste('Normalized', variant.names[i], sep='_'))
}
grid.arrange(grobs = p, ncol=2, nrow=2)
MA plots of all samples from condition 1 and condition 0.125 (quantification values averaged within condition).
# different unit variants require different computation of fold changes and average abuandance: additive or multiplicative scale; see maplot.ils function
channels.num <- colors.condition.map %>% filter(Condition=='1') %>% select(Channel) %>% pull
channels.denom <- colors.condition.map %>% filter(Condition=='0.125') %>% select(Channel) %>% pull
p <- vector('list', n.comp.variants+1)
p[[1]] <- maplot.ils(dat.nonnorm.summ.w2, channels.num, channels.denom, scale.vec[i], 'Raw')
for (i in 1: n.comp.variants){
p[[i+1]]<- maplot.ils(dat.norm.summ.w2[[i]], channels.num, channels.denom, scale.vec[i], paste('Normalized', variant.names[i], sep='_'))
}
grid.arrange(grobs = p, ncol=2, nrow=2)
dat.nonnorm.summ.l <- lapply(list(dat.nonnorm.summ.w), function(x){
x$Mixture <- unlist(lapply(stri_split(x$Run,fixed='_'), function(y) y[1]))
x <- to_long_format(x, study.design)
})
dat.norm.summ.l <- lapply(dat.norm.summ.w, function(x){
x$Mixture <- unlist(lapply(stri_split(x$Run,fixed='_'), function(y) y[1]))
x <- to_long_format(x, study.design)
})
par(mfrow=c(2, 2))
cvplot.ils(dat=dat.nonnorm.summ.l[[1]], feature.group='Protein', xaxis.group='Condition',
title='Raw', abs=F)
for (i in 1: n.comp.variants){
cvplot.ils(dat=dat.norm.summ.l[[i]], feature.group='Protein', xaxis.group='Condition',
title=paste('Normalized', variant.names[i], sep='_'), abs=F)
}
par(mfrow=c(1, 1))
# create a shorter version of run variable to present on legend ([-1] to avoid Protein col)
run.labels <- stri_replace(unlist(lapply(stri_split(colnames(dat.norm.summ.w2[[1]])[-1], fixed='_'), function(x) paste(x[2],x[3],sep = '_'))), fixed='Mixture', 'Mix')
par(mfrow=c(2, 2))
pcaplot.ils(dat.nonnorm.summ.w2 %>% select(-'Protein'), run.labels, conditions.vec, cols.vec, 'Raw')
for (i in seq_along(dat.norm.summ.w2)){
# select only the spiked.proteins
pcaplot.ils(dat.norm.summ.w2[[i]] %>% select(-'Protein'), run.labels, conditions.vec, cols.vec, paste('Normalized', variant.names[i], sep='_'))
}
par(mfrow=c(1, 1))
# create a shorter version of run variable to present on legend ([-1] to avoid Protein col)
run.labels <- stri_replace(unlist(lapply(stri_split(colnames(dat.norm.summ.w2[[1]])[-1], fixed='_'), function(x) paste(x[2],x[3],sep = '_'))), fixed='Mixture', 'Mix')
par(mfrow=c(2, 2))
pcaplot.ils(dat.nonnorm.summ.w2 %>% filter(Protein %in% spiked.proteins) %>% select(-'Protein'), run.labels, conditions.vec, cols.vec, 'Raw')
for (i in seq_along(dat.norm.summ.w2)){
# select only the spiked.proteins
pcaplot.ils(dat.norm.summ.w2[[i]] %>% filter(Protein %in% spiked.proteins) %>% select(-'Protein'), run.labels, conditions.vec, cols.vec, paste('Normalized', variant.names[i], sep='_'))
}
par(mfrow=c(1, 1))
Only use spiked proteins
TO DO: - also use short label names like in PCA plot - unify the list of args across pcaplot.ils and dendrogram.ils. Make sure labeling and color picking is done in the same location (either inside or outside the function)
sample.labels <- stri_replace(colnames(dat.nonnorm.summ.w2 %>% select(-Protein)), fixed='Mixture', 'Mix')
par(mfrow=c(2, 2))
dendrogram.ils(dat.nonnorm.summ.w2 %>% filter(Protein %in% spiked.proteins) %>% select(-Protein), sample.labels, cols.vec, 'Raw')
for (i in seq_along(dat.norm.summ.w2)){
dendrogram.ils(dat.norm.summ.w2[[i]] %>% filter(Protein %in% spiked.proteins) %>% select(-Protein), sample.labels, cols.vec, paste('Normalized', variant.names[i], sep='_'))
}
par(mfrow=c(1, 1))
TODO: - Also try to log-transform the intensity case, to see if there are large differences in the t-test results. - done. remove this code? NOTE: - actually, lmFit (used in moderated_ttest) was built for log2-transformed data. However, supplying untransformed intensities can also work. This just means that the effects in the linear model are also additive on the untransformed scale, whereas for log-transformed data they are multiplicative on the untransformed scale. Also, there may be a bias which occurs from biased estimates of the population means in the t-tests, as mean(X) is not equal to exp(mean(log(X))).
design.matrix <- get_design_matrix(referenceCondition, study.design)
dat.dea <- emptyList(names(dat.norm.summ.w2))
for (i in seq_along(dat.norm.summ.w2)) {
this_scale <- scale.vec[match(names(dat.dea)[i], variant.names)]
d <- column_to_rownames(as.data.frame(dat.norm.summ.w2[[i]]), 'Protein')
dat.dea[[i]] <- moderated_ttest(dat=d, design.matrix, scale=this_scale)
}
# also see what the unnormalized results would look like
n.comp.variants <- n.comp.variants + 1
variant.names <- c(variant.names, 'raw')
scale.vec <- c(scale.vec, 'raw')
dat.dea$raw <- moderated_ttest(dat=column_to_rownames(dat.nonnorm.summ.w2, 'Protein'), design.matrix, scale='raw')
cm <- conf.mat(dat.dea, 'q.mod', 0.05, spiked.proteins)
print.conf.mat(cm)
| background | spiked | background | spiked | background | spiked | background | spiked | |
|---|---|---|---|---|---|---|---|---|
| not_DE | 4063 | 15 | 4064 | 19 | 4064 | 18 | 4064 | 19 |
| DE | 1 | 4 | 0 | 0 | 0 | 1 | 0 | 0 |
| CONSTANd | NOMAD | medianSweeping | raw | |
|---|---|---|---|---|
| Accuracy | 0.996 | 0.995 | 0.996 | 0.995 |
| Sensitivity | 0.211 | 0.000 | 0.053 | 0.000 |
| Specificity | 1.000 | 1.000 | 1.000 | 1.000 |
| PPV | 0.800 | NaN | 1.000 | NaN |
| NPV | 0.996 | 0.995 | 0.996 | 0.995 |
| background | spiked | background | spiked | background | spiked | background | spiked | |
|---|---|---|---|---|---|---|---|---|
| not_DE | 4062 | 5 | 4064 | 15 | 4062 | 13 | 4064 | 19 |
| DE | 2 | 14 | 0 | 4 | 2 | 6 | 0 | 0 |
| CONSTANd | NOMAD | medianSweeping | raw | |
|---|---|---|---|---|
| Accuracy | 0.998 | 0.996 | 0.996 | 0.995 |
| Sensitivity | 0.737 | 0.211 | 0.316 | 0.000 |
| Specificity | 1.000 | 1.000 | 1.000 | 1.000 |
| PPV | 0.875 | 1.000 | 0.750 | NaN |
| NPV | 0.999 | 0.996 | 0.997 | 0.995 |
| background | spiked | background | spiked | background | spiked | background | spiked | |
|---|---|---|---|---|---|---|---|---|
| not_DE | 4055 | 3 | 4064 | 17 | 4063 | 10 | 4064 | 16 |
| DE | 9 | 16 | 0 | 2 | 1 | 9 | 0 | 3 |
| CONSTANd | NOMAD | medianSweeping | raw | |
|---|---|---|---|---|
| Accuracy | 0.997 | 0.996 | 0.997 | 0.996 |
| Sensitivity | 0.842 | 0.105 | 0.474 | 0.158 |
| Specificity | 0.998 | 1.000 | 1.000 | 1.000 |
| PPV | 0.640 | 1.000 | 0.900 | 1.000 |
| NPV | 0.999 | 0.996 | 0.998 | 0.996 |
TO DO: Piotr: constant NOMAD i RAW q-values (approx. 1) generate error in scatterplots
# character vectors containing logFC and p-values columns
dea.cols <- colnames(dat.dea[[1]])
logFC.cols <- dea.cols[stri_detect_fixed(dea.cols, 'logFC')]
q.cols <- dea.cols[stri_detect_fixed(dea.cols, 'q.mod')]
n.contrasts <- length(logFC.cols)
#scatterplot.ils(dat.dea, q.cols, 'p-values') # commented due to error, sd=0 for NOMAD and RAW
scatterplot.ils(dat.dea, logFC.cols, 'log2FC')
for (i in 1:n.contrasts){
volcanoplot.ils(dat.dea, i, spiked.proteins)
}
Let’s see whether the spiked protein fold changes make sense
# plot theoretical value (horizontal lines) and violin per condition
dat.spiked.logfc <- lapply(dat.dea, function(x) x[spiked.proteins,logFC.cols])
dat.spiked.logfc.l <- lapply(dat.spiked.logfc, function(x) {
x %>% rename_with(function(y) sapply(y, function(z) strsplit(z, '_')[[1]][2])) %>% pivot_longer(cols = everything(), names_to = 'condition', values_to = 'logFC') %>% add_column(Protein=rep(rownames(x), each=length(colnames(x)))) })
violinplot.ils(lapply(dat.spiked.logfc.l, filter, condition != referenceCondition))
sessionInfo()
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=de_BE.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=de_BE.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=de_BE.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=de_BE.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] dendextend_1.14.0 NOMAD_0.99.0 dplR_1.7.1 CONSTANd_0.99.4
## [5] forcats_0.5.0 stringr_1.4.0 dplyr_1.0.2 purrr_0.3.4
## [9] readr_1.4.0 tidyr_1.1.2 tibble_3.0.4 tidyverse_1.3.0
## [13] limma_3.45.19 psych_2.0.9 kableExtra_1.3.1 gridExtra_2.3
## [17] stringi_1.5.3 ggplot2_3.3.2
##
## loaded via a namespace (and not attached):
## [1] nlme_3.1-150 matrixStats_0.57.0 fs_1.5.0
## [4] lubridate_1.7.9 webshot_0.5.2 httr_1.4.2
## [7] tools_4.0.3 backports_1.1.10 R6_2.4.1
## [10] rpart_4.1-15 DBI_1.1.0 mgcv_1.8-33
## [13] colorspace_1.4-1 nnet_7.3-14 withr_2.3.0
## [16] tidyselect_1.1.0 mnormt_2.0.2 compiler_4.0.3
## [19] cli_2.1.0 rvest_0.3.6 xml2_1.3.2
## [22] labeling_0.4.2 scales_1.1.1 digest_0.6.27
## [25] rmarkdown_2.5 R.utils_2.10.1 pkgconfig_2.0.3
## [28] htmltools_0.5.0 highr_0.8 dbplyr_1.4.4
## [31] rlang_0.4.8 readxl_1.3.1 rstudioapi_0.11
## [34] generics_0.0.2 farver_2.0.3 jsonlite_1.7.1
## [37] ModelMetrics_1.2.2.2 R.oo_1.24.0 magrittr_1.5
## [40] Matrix_1.2-18 Rcpp_1.0.5 munsell_0.5.0
## [43] fansi_0.4.1 viridis_0.5.1 lifecycle_0.2.0
## [46] R.methodsS3_1.8.1 pROC_1.16.2 yaml_2.2.1
## [49] MASS_7.3-53 plyr_1.8.6 recipes_0.1.14
## [52] grid_4.0.3 blob_1.2.1 parallel_4.0.3
## [55] crayon_1.3.4 lattice_0.20-41 haven_2.3.1
## [58] splines_4.0.3 hms_0.5.3 tmvnsim_1.0-2
## [61] knitr_1.30 pillar_1.4.6 stats4_4.0.3
## [64] reshape2_1.4.4 codetools_0.2-16 reprex_0.3.0
## [67] XML_3.99-0.5 glue_1.4.2 evaluate_0.14
## [70] data.table_1.13.2 modelr_0.1.8 png_0.1-7
## [73] vctrs_0.3.4 foreach_1.5.1 cellranger_1.1.0
## [76] gtable_0.3.0 assertthat_0.2.1 gower_0.2.2
## [79] xfun_0.18 prodlim_2019.11.13 broom_0.7.2
## [82] e1071_1.7-4 survival_3.2-7 class_7.3-17
## [85] viridisLite_0.3.0 timeDate_3043.102 signal_0.7-6
## [88] iterators_1.0.13 lava_1.6.8 ellipsis_0.3.1
## [91] caret_6.0-86 ipred_0.9-9